Expert-free eye alignment and machine learning for predictive health
Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2017.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2017
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| Online Access: | http://hdl.handle.net/1721.1/112543 |
| _version_ | 1811089338342047744 |
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| author | Swedish, Tristan Breaden |
| author2 | Ramesh Raskar. |
| author_facet | Ramesh Raskar. Swedish, Tristan Breaden |
| author_sort | Swedish, Tristan Breaden |
| collection | MIT |
| description | Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2017. |
| first_indexed | 2024-09-23T14:17:37Z |
| format | Thesis |
| id | mit-1721.1/112543 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T14:17:37Z |
| publishDate | 2017 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1125432019-04-12T22:56:39Z Expert-free eye alignment and machine learning for predictive health Swedish, Tristan Breaden Ramesh Raskar. Program in Media Arts and Sciences (Massachusetts Institute of Technology) Program in Media Arts and Sciences (Massachusetts Institute of Technology) Program in Media Arts and Sciences () Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2017. Cataloged from PDF version of thesis. Includes bibliographical references (pages 67-72). This thesis documents the development of an "expert-free" device in order to realize a system for scalable screening of the eye fundus. The goal of this work is to demonstrate enabling technologies that remove dependence on expert operators and explore the usefulness of this approach in the context of scalable health screening. I will present a system that includes a novel method for eye self-alignment and automatic image analysis and evaluate its effectiveness when applied to a case study of a diabetic retinopathy screening program. This work is inspired by advances in machine learning that makes accessible interactions previously confined to specialized environments and trained users. I will also suggest some new directions for future work based on this expert-free paradigm. by Tristan Breaden Swedish. S.M. 2017-12-05T19:17:34Z 2017-12-05T19:17:34Z 2017 2017 Thesis http://hdl.handle.net/1721.1/112543 1012944585 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 72 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Program in Media Arts and Sciences () Swedish, Tristan Breaden Expert-free eye alignment and machine learning for predictive health |
| title | Expert-free eye alignment and machine learning for predictive health |
| title_full | Expert-free eye alignment and machine learning for predictive health |
| title_fullStr | Expert-free eye alignment and machine learning for predictive health |
| title_full_unstemmed | Expert-free eye alignment and machine learning for predictive health |
| title_short | Expert-free eye alignment and machine learning for predictive health |
| title_sort | expert free eye alignment and machine learning for predictive health |
| topic | Program in Media Arts and Sciences () |
| url | http://hdl.handle.net/1721.1/112543 |
| work_keys_str_mv | AT swedishtristanbreaden expertfreeeyealignmentandmachinelearningforpredictivehealth |